Federated Meta-Learning for task offloading and resource allocation in MEC-IoT

MEC and the Internet of Things (IoT) are two areas rapidly expanding technologies that offer many opportunities to improve efficiency and application performance. However, the huge amount of data generated by IoT devices and the processing and latency constraints imposed by these technologies and mobile networks make processing this data a major challenge. As part of MEC architecture, a promising approach to solving this challenge is to deploy computing servers at the edge of the network, close to IoT devices. This makes it possible to reduce latency and traffic load on the core network, while offering a better user experience. However, offloading tasks from IoT devices to the MEC servers and efficiently allocating the available computing resources is a complex problem. IoT tasks can have latency, bandwidth, and speed requirements, resources, and available computational resources may be limited or shared between multiple users. To solve this problem, we propose an approach based on federated meta-learning. In our approach, each device IoT collects information about the tasks to be performed and the local resources available, then securely shares them with a local MEC server. The local server uses these information to train a meta-learning model that can predict the best task offload and resource allocation decisions for each task. The advantage part of our approach is that it allow us to learn from the experiences of all IoT devices, resulting in more robust and accurate models. We evaluated our FedMeta2Ag algorithm using the MNIST database (Modified National Institute of Standards and Technology database). Considering 20 epochs, the accuracy during training is 91.5% against 92.0% obtained with the test data. In addition, performance continues to increase during the first 20 iterations and gradually becomes stable. Moreover, the accuracy evaluation involves the comparison of the proposed approach with existing methods. This comparison reveals that our approach predict the performance with more accuracy than the existing models. This approach can effectively fulfil the demanding performance of wireless communications systems.